Simulation under uncertainty

Abstract

Effects of uncertain random elements within systems are expected to be very relevant in future energy systems due to the volatility of pervasive new energy sources (particularly large and small renewable sources, and consumer-owned small sources) and the presence of communication networks (e.g. random communication delays). These uncertain phenomena add to uncertainty sources typical of classical energy networks: load behavior, prices, and components reliability. Uncertainty can be introduced into systems by processes that are too complicated to model deterministically (i.e. exactly), and thus the processes are assumed to be stochastic (i.e. random). Moreover, the uncertainty of a system can come from having limited exact knowledge of elements of said system leading to these elements to be considered random. As uncertain/random quantities can impact the behavior of a system, modeling methods for uncertainty quantification are of great importance and should be considered at all stages of design and operation, including supporting real-time optimization and control.

Authors 3

1. Matthew Milton Aachen

   RWTH Aachen University · Forschungszentrum Jülich · Fraunhofer Institute for Applied Information Technology · University of South Carolina

   Affiliation as printed

   Department of Electrical Engineering

   Department of Mechanical Engineering

   Fraunhofer FIT Center for Digital Energy

   Institute for Automation of Complex Power Systems

   Institute of Energy and Climate Research: Energy Systems Engineering (IEK-10)

   Juelich Research Center

   RWTH Aachen University

   University of South Carolina

   Department of Electrical Engineering, University of South Carolina, , USA

2. Andrea Benigni Aachen

   RWTH Aachen University · Forschungszentrum Jülich · Fraunhofer Institute for Applied Information Technology · University of South Carolina

   Affiliation as printed

   Department of Electrical Engineering

   Department of Mechanical Engineering

   Fraunhofer FIT Center for Digital Energy

   Institute for Automation of Complex Power Systems

   Institute of Energy and Climate Research: Energy Systems Engineering (IEK-10)

   Juelich Research Center

   RWTH Aachen University

   University of South Carolina

   Institute of Energy and Climate Research: Energy Systems Engineering (IEK-10), Juelich Research Center, , Germany

3. Antonello Monti Aachen Institute for Automation of Complex Power Systems

   RWTH Aachen University · Forschungszentrum Jülich · Fraunhofer Institute for Applied Information Technology · University of South Carolina

   Affiliation as printed

   Department of Electrical Engineering

   Department of Mechanical Engineering

   Fraunhofer FIT Center for Digital Energy

   Institute for Automation of Complex Power Systems

   Institute of Energy and Climate Research: Energy Systems Engineering (IEK-10)

   Juelich Research Center

   RWTH Aachen University

   University of South Carolina

   Institute for Automation of Complex Power Systems, RWTH Aachen University, , Germany